SoroushVahidi/scidocs huggingface dataset:
Personal Attributes Extraction in Chinese Text Based on Recurrent Neural Network
(2017)

Personal Attributes Extraction in Chinese Text Task is designed to extract person specific attributes, for example, spouse/husband, children, education, title etc. from unstructured Chinese texts. According to the characteristics of the personal attributes extraction, we propose a new method based on deep learning to extract attributes from the documents in this paper. Experiments show that this method can improve the accuracy of personal attributes extraction effectively.
Strategic Human Resources Management : Where Do We Go From Here ? †
(2006)

The authors identify the key challenges facing strategic human resource management (SHRM) going forward and discuss several new directions in both the scholarship and practice of SHRM. They focus on a clearer articulation of the “black box” between HR and firm performance, emphasizing the integration of strategy implementation as the central mediating variable in this relationship. There are direct implications for the nature of fit and contingencies in SHRM. They also highlight the significance of a differentiated HR architecture not just across firms but also within firms.
Edlines: Real-time line segment detection by Edge Drawing (ed)
(2011)

We propose a linear time line segment detector that gives accurate results, requires no parameter tuning, and runs up to 11 times faster than the fastest known line segment detection algorithm in the literature; namely, the LSD by Gioi et al. The proposed algorithm also includes a line validation step due to the Helmholtz principle, which lets it control the number of false detections. Our detector makes use of the clean, contiguous (connected) chain of edge pixels produced by our novel edge detector, the Edge Drawing (ED) algorithm; hence the name EDLines. With its accurate results and blazing speed, EDLines will be very suitable for the next generation real-time computer vision applications.
Who does what during a code review? Datasets of OSS peer review repositories
(2013)

We present four datasets that are focused on the general roles of OSS peer review members. With data mined from both an integrated peer review system and code source repositories, our rich datasets comprise of peer review data that was automatically recorded. Using the Android project as a case study, we describe our extraction methodology, the datasets and their application used for three separate studies. Our datasets are available online at http://sdlab.naist.jp/reviewmining/.
HEXHOOP: Modular Templates for Converting a Hex-Dominant Mesh to an ALL-Hex Mesh
(2001)

This paper presents a new mesh conversion template called HEXHOOP, which fully automates a con-version from a hex-dominant mesh to an all-hex mesh. A HEXHOOP template subdivides a hex/prism/pyramid element into a set of smaller hex elements while main-taining the topological conformity with neighboring elements. A HEXHOOP template is constructed by assembling sub-templates, cores and caps. A dicing template for a hex and a prism is constructed by choosing the appropriate combination of a core and caps. A template that dices a pyramid without losing conformity to the adjacent element is derived from a HEXHOOP template. Some experimental results show that the HEXHOOP templates successfully convert a hex-dominant mesh into an all-hex mesh.
Chat Speed OP : Practices of Coherence in Massive Twitch Chat
(2017)

Paste the appropriate copyright statement here. ACM now supports three different copyright statements: • ACM copyright: ACM holds the copyright on the work. This is the historical approach. • License: The author(s) retain copyright, but ACM receives an exclusive publication license. • Open Access: The author(s) wish to pay for the work to be open access. The additional fee must be paid to ACM. This text field is large enough to hold the appropriate release statement assuming it is single spaced in a sans-serif 7 point font. Every submission will be assigned their own unique DOI string to be included here. Abstract Twitch.tv, a streaming platform known for video game content, has grown tremendously since its inception in 2011. We examine communication practices in Twitch chats for the popular game Hearthstone, comparing massive chats with at least 10,000 concurrent viewers and small chats with fewer than 2,000 concurrent viewers. Due to the large scale and fast pace of massive chats, communication patterns no longer follow models developed in previous studies of computer-mediated communication. Rather than what other studies have described as communication breakdowns and information overload, participants in massive chats communicate in what we call “crowdspeak.”
A Mobile Transaction Model That Captures Both the Data and Movement Behavior
(1997)

Unlike distributed transactions, mobile transactions do not originate and end at the same site. The implication of the movement of such transactions is that classical atomicity, concurrency and recovery solutions must be revisited to capture the movement behavior. As an effort in this direction, we define a model of mobile transactions by building on the concepts of split transactions and global transactions in a multidatabase environment. Our view of mobile transactions, called Kangaroo Transactions, incorporates the property that transactions in a mobile computing system hop from one base station to another as the mobile unit moves through cells. Our model is the first to capture this movement behavior as well as the data behavior which reflects the access to data located in databases throughout the static network. The mobile behavior is dynamic and is realized in our model via the use of split operations. The data access behavior is captured by using the idea of global and local transactions in a multidatabase system.
Birds of a ‘ ‘ bad ’ ’ feather flock together : The Dark Triad and mate choice
(2015)

Previous research on the Dark Triad traits (i.e., Machiavellianism, psychopathy, and narcissism) has focused solely on the role the Dark Triad traits played in mate choice of actors. The current study (N = 336) extends this by manipulating the apparent levels of Dark Triad traits in targets and correlating mate choice in these targets with individual differences in the Dark Triad traits in actors. As expected, both sexes preferred partners low in the Dark Triad traits for long-term mating, while those high in these traits were preferred for one-night stands. However, women high in psychopathy considered the Dark Triad traits in potential male partners more physically attractive and desirable for an one-night stand, as well as a potential husband. Men who were high on psychopathy were likewise attracted to psychopathy in potential mothers. Our findings are discussed from an evolutionary personality paradigm. 2015 Elsevier Ltd. All rights reserved.
Fast Metric Tracking by Detection System: Radar Blob and Camera Fusion
(2015)

This article proposes a system that fuses radar and monocular vision sensor data in order to detect and classify on-road obstacles, like cars or not cars (other obstacles). The obstacle detection process and classification is divided into three stages, the first consist in reading radar signals and capturing the camera data, the second stage is the data fusion, and the third step is the classify the obstacles, aiming to differentiate the obstacles types identified by the radar and confirmed by the computer vision. In the detection task it is important to locate, measure, and rank the obstacles to be able to take adequate decisions and actions (e.g. Generate alerts, autonomously control the vehicle path), so the correct classification of the obstacle type and position, is very important, also avoiding false positives and/or false negatives in the classification task.
Learning Distributed Representations of Texts and Entities from Knowledge Base
(2017)

We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.
Automatic removal of manually induced artefacts in ultrasound images of thyroid gland
(2013)

Manually induced artefacts, like caliper marks and anatomical labels, render an ultrasound (US) image incapable of being subjected to further processes of Computer Aided Diagnosis (CAD). In this paper, we propose a technique to remove these artefacts and restore the image as accurately as possible. The technique finds application as a pre-processing step when developing unsupervised segmentation algorithms for US images that deal with automatic estimation of the number of segments and clustering. The novelty of the algorithm lies in the image processing pipeline chosen to automatically identify the artefacts and is developed based on the histogram properties of the artefacts. The algorithm was able to successfully restore the images to a high quality when it was executed on a dataset of 18 US images of the thyroid gland on which the artefacts were induced manually by a doctor. Further experiments on an additional dataset of 10 unmarked US images of the thyroid gland on which the artefacts were simulated using Matlab showed that the restored images were again of high quality with a PSNR > 38dB and free of any manually induced artefacts.
Comparison of automatic shot boundary detection algorithms
(1998)

Various methods of automatic shot boundary detection have been proposed and claimed to perform reliably. A the detection of edits is fundamental to any kind of video analysis since it segments a video into its basic com the shots, only few comparative investigations on early shot boundary detection algorithms have been pu These investigations mainly concentrate on measuring the edit detection performance, however, do not con algorithms’ ability to classify the types and to locate the boundaries of the edits correctly. This paper extend comparative investigations. More recent algorithms designed explicitly to detect specific complex editing ope such as fades and dissolves are taken into account, and their ability to classify the types and locate the boun such edits are examined. The algorithms’ performance is measured in terms of hit rate, number of false hits, a rate for hard cuts, fades, and dissolves over a large and diverse set of video sequences. The experiments while hard cuts and fades can be detected reliably, dissolves are still an open research issue. The false hit rat solves is usually unacceptably high, ranging from 50% up to over 400%. Moreover, all algorithms seem to fai roughly the same conditions.
Bayesian Classification (AutoClass): Theory and Results
(1996)

We describe AutoClass, an approach to unsupervised classiication based upon the classical mixture model, supplemented by a Bayesian method for determining the optimal classes. We include a moderately detailed exposition of the mathematics behind the AutoClass system. We emphasize that no current unsupervised classiication system can produce maximally useful results when operated alone. It is the interaction between domain experts and the machine searching over the model space, that generates new knowledge. Both bring unique information and abilities to the database analysis task, and each enhances the others' eeectiveness. We illustrate this point with several applications of AutoClass to complex real world databases, and describe the resulting successes and failures. 6.1 Introduction This chapter is a summary of our experience in using an automatic classiication program (AutoClass) to extract useful information from databases. It also gives an outline of the principles that underlie automatic classiication in general, and AutoClass in particular. We are concerned with the problem of automatic discovery of classes in data (sometimes called clustering, or unsupervised learning), rather than the generation of class descriptions from labeled examples (called supervised learning). In some sense, automatic classiication aims at discovering the \natural" classes in the data. These classes reeect basic causal mechanisms that makes some cases look more like each other than the rest of the cases. The causal mechanisms may be as boring as sample biases in the data, or could reeect some major new discovery in the domain. Sometimes, these classes were well known to experts in the eld, but unknown to AutoClass, and other times
Context dependent recurrent neural network language model
(2012)

Recurrent neural network language models (RNNLMs) have recently demonstrated state-of-the-art performance across a variety of tasks. In this paper, we improve their performance by providing a contextual real-valued input vector in association with each word. This vector is used to convey contextual information about the sentence being modeled. By performing Latent Dirichlet Allocation using a block of preceding text, we achieve a topic-conditioned RNNLM. This approach has the key advantage of avoiding the data fragmentation associated with building multiple topic models on different data subsets. We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art. We further apply the model to the Wall Street Journal speech recognition task, where we observe improvements in word-error-rate.
DyNet: The Dynamic Neural Network Toolkit
(2017)

We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives. In DyNet’s dynamic declaration strategy, computation graph construction is mostly transparent, being implicitly constructed by executing procedural code that computes the network outputs, and the user is free to use different network structures for each input. Dynamic declaration thus facilitates the implementation of more complicated network architectures, and DyNet is specifically designed to allow users to implement their models in a way that is idiomatic in their preferred programming language (C++ or Python). One challenge with dynamic declaration is that because the symbolic computation graph is defined anew for every training example, its construction must have low overhead. To achieve this, DyNet has an optimized C++ backend and lightweight graph representation. Experiments show that DyNet’s speeds are faster than or comparable with static declaration toolkits, and significantly faster than Chainer, another dynamic declaration toolkit. DyNet is released opensource under the Apache 2.0 license, and available at http://github.com/clab/dynet. Carnegie Mellon University, Pittsburgh, PA, USA Nara Institute of Science and Technology, Ikoma, Japan DeepMind, London, UK Bar Ilan University, Ramat Gan, Israel Allen Institute for Artificial Intelligence, Seattle, WA, USA University of Notre Dame, Notre Dame, IN, USA IBM T.J. Watson Research Center, Yorktown Heights, NY, USA University of Melbourne, Melbourne, Australia Johns Hopkins University, Baltimore, MD, USA Google, New York, NY, USA Google, Mountain View, CA, USA University of Washington, Seattle, USA Microsoft Research, Redmond, WA, USA University of Edinburgh, Edinburgh, UK 1 ar X iv :1 70 1. 03 98 0v 1 [ st at .M L ] 1 5 Ja n 20 17
KenLM: Faster and Smaller Language Model Queries
(2011)

We present KenLM, a library that implements two data structures for efficient language model queries, reducing both time and memory costs. The PROBING data structure uses linear probing hash tables and is designed for speed. Compared with the widelyused SRILM, our PROBING model is 2.4 times as fast while using 57% of the memory. The TRIE data structure is a trie with bit-level packing, sorted records, interpolation search, and optional quantization aimed at lower memory consumption. TRIE simultaneously uses less memory than the smallest lossless baseline and less CPU than the fastest baseline. Our code is open-source1, thread-safe, and integrated into the Moses, cdec, and Joshua translation systems. This paper describes the several performance techniques used and presents benchmarks against alternative implementations.
Off-policy evaluation for slate recommendation
(2017)

This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context—a common scenario in web search, ads, and recommendation. We build on techniques from combinatorial bandits to introduce a new practical estimator that uses logged data to estimate a policy’s performance. A thorough empirical evaluation on real-world data reveals that our estimator is accurate in a variety of settings, including as a subroutine in a learningto-rank task, where it achieves competitive performance. We derive conditions under which our estimator is unbiased—these conditions are weaker than prior heuristics for slate evaluation—and experimentally demonstrate a smaller bias than parametric approaches, even when these conditions are violated. Finally, our theory and experiments also show exponential savings in the amount of required data compared with general unbiased estimators.
Towards a functional neuroanatomy of speech perception
(2000)

The functional neuroanatomy of speech perception has been difficult to characterize. Part of the difficulty, we suggest, stems from the fact that the neural systems supporting 'speech perception' vary as a function of the task. Specifically, the set of cognitive and neural systems involved in performing traditional laboratory speech perception tasks, such as syllable discrimination or identification, only partially overlap those involved in speech perception as it occurs during natural language comprehension. In this review, we argue that cortical fields in the posterior-superior temporal lobe, bilaterally, constitute the primary substrate for constructing sound-based representations of speech, and that these sound-based representations interface with different supramodal systems in a task-dependent manner. Tasks that require access to the mental lexicon (i.e. accessing meaning-based representations) rely on auditory-to-meaning interface systems in the cortex in the vicinity of the left temporal-parietal-occipital junction. Tasks that require explicit access to speech segments rely on auditory-motor interface systems in the left frontal and parietal lobes. This auditory-motor interface system also appears to be recruited in phonological working memory.
Path planning for UAV based on improved heuristic A∗ algorithm
(2009)

A 3D vehicle path planning method based on heuristic A∗ algorithm is proposed and simulated which makes the UAV's path planning for low altitude penetration more real-time, effective and engineering-oriented. The digital terrain information is processed with the integrated terrain elevation smoothing algorithm at first, and then, the safe flight surface which integrates the maneuverability of vehicle is built, At last, the mathematical models of the threats are established. An improved A∗ algorithm is used to plan 3D vehicle path on the safe surface, and the planned path is optimized and smoothed. Simulation results indicate that the method has a good performance in creating optimal path in 3D environment and what's more, the path planning algorithm is simple, efficient and easily realized in engineering field, thus adapting to the different missions of the UAV.
Vaginoplasty using autologous in vitro cultured vaginal tissue in a patient with Mayer-von-Rokitansky-Kuster-Hauser syndrome.
(2007)

Mayer-von-Rokitansky-Küster-Hauser syndrome (MRKHS) is characterized by vaginal agenesis with variable Müllerian duct abnormalities. The Abbè-McIndoe technique is considered a valid treatment option for vaginoplasty but no consensus has been reached on what material should be used for the neovagina canal wall lining. We report the first case of autologous vaginal tissue transplantation in a 28-year-old women with MRKHS. The patient was subjected to a 1 cm2 full-thickness mucosal biopsy from the vaginal vestibule. Following enzymatic dissociation, cells were inoculated onto collagen IV-coated plates and cultured for 2 weeks. The patient was subjected to a vaginoplasty with a modified Abbè-McIndoe vaginoplasty with 314 cm2 autologous in vitro cultured vaginal tissue for the canal lining. At 1 month from surgery, the vagina appeared normal in length and depth and a vaginal biopsy revealed normal vaginal tissue. The use of autologous in vitro cultured vaginal tissue to create a neovagina appears as an easy, minimally invasive and useful method.
Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery
(2016)

In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain-computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.
SwiPIN: Fast and Secure PIN-Entry on Smartphones
(2015)

In this paper, we present SwiPIN, a novel authentication system that allows input of traditional PINs using simple touch gestures like up or down and makes it secure against human observers. We present two user studies which evaluated different designs of SwiPIN and compared it against traditional PIN. The results show that SwiPIN performs adequately fast (3.7 s) to serve as an alternative input method for risky situations. Furthermore, SwiPIN is easy to use, significantly more secure against shoulder surfing attacks and switching between PIN and SwiPIN feels natural.